Color image segmentation based on 3-D clustering: morphological approach

In this paper, a new segmentation algorithm for color images based on mathematical morphology is presented. Color image segmentation is essentially a clustering process in 3-D color space, but the characteristics of clusters vary severely, according to the type of images and color coordinates. Hence, the methodology employs the scheme of thresholding the difference of Gaussian smoothed 3-D histogram to get the initial seeds for clustering, and then uses a closing operation and adaptive dilation to extract the number of clusters and their representative values, and to include the suppressed bins during Gaussian smoothing, without a priori knowledge on the image. This procedure also implicitly takes into account the statistical properties, such as the shape, connectivity and distribution of clusters. Intensive computer simulation has been performed and the results are discussed in this paper. The results of the simulation show that the proposed segmentation algorithm is independent of the choice of color coordinates, the shape of clusters, and the type of images. The segmentation results using the k-means technique are also presented for comparison purposes.